A validated multi-method census of 180M repositories shows AI coding agents generate over 320k commits per month, with bot-account detection recovering only 3.3% of Claude Code activity and commit/PR channels capturing disjoint populations.
Detecting LLM-generated code with subtle modification by adversarial training.arXiv preprint arXiv:2507.13123, 2025
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OOM-RL aligns multi-agent LLM systems for software engineering by using real financial market losses as an un-hackable negative gradient, resulting in a mature-phase annualized Sharpe ratio of 2.06 via a strict test-driven workflow.
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Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories
A validated multi-method census of 180M repositories shows AI coding agents generate over 320k commits per month, with bot-account detection recovering only 3.3% of Claude Code activity and commit/PR channels capturing disjoint populations.
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OOM-RL: Out-of-Money Reinforcement Learning Market-Driven Alignment for LLM-Based Multi-Agent Systems
OOM-RL aligns multi-agent LLM systems for software engineering by using real financial market losses as an un-hackable negative gradient, resulting in a mature-phase annualized Sharpe ratio of 2.06 via a strict test-driven workflow.